A Live-User Study of Opinionated Explanations for Recommender Systems

This paper describes an approach for generating rich and compelling
explanations in recommender systems, based on opinions
mined from user-generated reviews. The explanations
highlight the features of a recommended item that matter most
to the user and also relate them to other recommendation alternatives
and the user‘s past activities to provide a context.

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